面向飞行试验的多源气动数据智能融合方法

王旭; 宁晨伽; 王文正; 张伟伟

doi:10.7638/kqdlxxb-2021.0428

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面向飞行试验的多源气动数据智能融合方法

研究论文 | 更新时间：2026-05-28

- 面向飞行试验的多源气动数据智能融合方法
- Intelligent fusion method of multi-source aerodynamic data for flight tests
- 空气动力学学报 2023年41卷第2期页码：12-20
- 作者机构：
  
  1. 西北工业大学航空学院,西安,710072
  2. 电子科技大学航空航天学院,成都,611731
- 作者简介：
  
  王旭（1996-），男，河南安阳人，博士研究生，研究方向：主要从事多源气动数据建模与流动控制研究. E-mail：wxnpu2014@163.com
  张伟伟*（1979-），教授，研究方向：主要从事智能流体力学/气动弹性力学研究. E-mail：aeroelastic@nwpu.edu.cn
- 基金信息：
- DOI：10.7638/kqdlxxb-2021.0428
  中图分类号： O354;V211.5
- 纸质出版：2023
- 稿件说明：
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王旭, 宁晨伽, 王文正, 等. 面向飞行试验的多源气动数据智能融合方法[J]. 空气动力学学报, 2023, 41(2): 12−20.

WANG Xu, NING Chenjia, WANG Wenzheng, et al. Intelligent fusion method of multi-source aerodynamic data for flight tests[J]. 2023, 41(2): 12-20.
王旭, 宁晨伽, 王文正, 等. 面向飞行试验的多源气动数据智能融合方法[J]. 空气动力学学报, 2023, 41(2): 12−20. DOI： 10.7638/kqdlxxb-2021.0428

WANG Xu, NING Chenjia, WANG Wenzheng, et al. Intelligent fusion method of multi-source aerodynamic data for flight tests[J]. 2023, 41(2): 12-20. DOI： 10.7638/kqdlxxb-2021.0428.

摘要

风洞试验和飞行试验是飞行器研制过程中进行气动性能分析与优化设计的重要手段，然而，在高超声速飞行条件下，真实气体效应、黏性干扰效应和尺度效应的复杂变化给气动数据精准预测带来巨大挑战。为了提升天地气动数据一致性，针对某外形飞行试验数据开展了典型对象的天地气动数据融合方法研究。结合数据挖掘的随机森林方法，本文提出了一种面向飞行试验的数据融合框架，通过引入地面风洞试验气动数据，实现了对复杂输入参数的特征分析与特征排序，进一步对不同飞行时刻下飞行试验的气动数据开展了交叉验证。结果表明随机森林的机器学习框架对风洞-飞行试验数据关联具有较好的预测与外推能力，可以有效提升气动数据预测精度，相关研究为复杂环境下气动数据多源融合提供了思路。

Abstract

Wind tunnel and flight tests are two of the most important methods for aerodynamic analyses and optimization design during the development of aircraft. However

under hypersonic flight conditions

the real gas effects

viscous interference effects

and multi-scale flow fields pose huge challenges to aerodynamic prediction. To improve the consistency of aerodynamic data between wind-tunnel and flight tests

an aerodynamic data fusion framework based on the random forest method for data mining is proposed and applied in the aerodynamic data fusion of a hypersonic aircraft. Feature analyses and ranking of aerodynamic data obtained by ground wind tunnel tests are conducted first. Then aerodynamic data in a flight envelop are cross-validated. Results show that the machine learning framework based on the random forest has good prediction and extrapolation capabilities for the correlation of aerodynamic data obtained by wind-tunnel and flight tests

and can effectively improve the prediction accuracy of aerodynamic data. The method provides a promising solution to the multi-source fusion of aerodynamic data in complex environments.

关键词

Keywords

references

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